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https://github.com/prowler-cloud/prowler.git
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fix(Dashboard): Multiple dashboard fixes (#3654)
Co-authored-by: Sergio Garcia <38561120+sergargar@users.noreply.github.com>
This commit is contained in:
Pedro Martín
+21
-17
@@ -35,7 +35,7 @@ dashboard = dash.Dash(
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# Logo
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prowler_logo = html.Img(
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src="https://prowler.com/logo-dashboard.png", alt="Prowler Logo"
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src="https://prowler.com/wp-content/uploads/logo-dashboard.png", alt="Prowler Logo"
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)
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menu_icons = {
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@@ -119,7 +119,9 @@ dashboard.layout = html.Div(
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# Placeholder for dynamic navigation bar
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html.Div(
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[
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html.Div(id="navigation-bar", className="bg-prowler-stone-900"),
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html.Div(
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id="navigation-bar", className="bg-prowler-stone-900 min-w-36 z-10"
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),
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html.Div(
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[
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dash.page_container,
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@@ -149,22 +151,24 @@ def update_nav_bar(pathname):
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[html.Ul(generate_nav_links(pathname), className="")],
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className="flex flex-col gap-y-6",
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),
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html.A(
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[
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html.Span(
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[
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html.Img(src="assets/favicon.ico", className="w-5"),
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"Subscribe to prowler SaaS",
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],
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className="flex items-center gap-x-3",
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),
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],
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href="https://prowler.com/",
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target="_blank",
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className="block mt-300 px-3 py-3 uppercase text-xs hover:bg-prowler-stone-950 hover:border-r-4 hover:border-solid hover:border-prowler-lime",
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),
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html.Nav(
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[html.Ul(generate_help_menu(), className="")],
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[
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html.A(
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[
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html.Span(
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[
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html.Img(src="assets/favicon.ico", className="w-5"),
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"Subscribe to prowler SaaS",
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],
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className="flex items-center gap-x-3",
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),
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],
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href="https://prowler.com/",
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target="_blank",
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className="block p-3 uppercase text-xs hover:bg-prowler-stone-950 hover:border-r-4 hover:border-solid hover:border-prowler-lime",
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),
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html.Ul(generate_help_menu(), className=""),
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],
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className="flex flex-col gap-y-6 mt-auto",
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),
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],
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+16
@@ -660,6 +660,10 @@ video {
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position: relative;
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}
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.z-10 {
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z-index: 10;
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}
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.col-span-11 {
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grid-column: span 11 / span 11;
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}
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@@ -729,6 +733,14 @@ video {
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display: inline-flex;
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}
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.min-w-36 {
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min-width: 9rem;
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}
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.min-w-44 {
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min-width: 11rem;
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}
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.table {
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display: table;
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}
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@@ -894,6 +906,10 @@ video {
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background-image: linear-gradient(127.43deg, #F1F5F8 -177.68%, #636c78 87.35%);
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}
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.p-3 {
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padding: 0.75rem;
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}
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.p-2 {
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padding: 0.5rem;
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}
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+52
-43
@@ -61,7 +61,8 @@ def load_csv_files(csv_files):
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for file in csv_files:
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df = pd.read_csv(file, sep=";", on_bad_lines="skip")
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if "CHECK_ID" in df.columns:
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dfs.append(df.astype(str))
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if "TIMESTAMP" in df.columns or df["PROVIDER"].unique() == "aws":
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dfs.append(df.astype(str))
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# Handle the case where there are no files
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try:
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data = pd.concat(dfs, ignore_index=True)
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@@ -101,35 +102,43 @@ else:
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data["ASSESSMENT_START_TIME"] = data["ASSESSMENT_START_TIME"].str.replace(
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"T", " "
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)
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# for each row, we are going to take the ASSESMENT_START_TIME if is not null and put it in the TIMESTAMP column
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data.rename(columns={"ASSESSMENT_START_TIME": "TIMESTAMP_AUX"}, inplace=True)
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# Unify the columns
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data["TIMESTAMP"] = data.apply(
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lambda x: (
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x["ASSESSMENT_START_TIME"]
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if pd.isnull(x["TIMESTAMP"])
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else x["TIMESTAMP"]
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x["TIMESTAMP_AUX"] if pd.isnull(x["TIMESTAMP"]) else x["TIMESTAMP"]
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),
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axis=1,
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)
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if "ACCOUNT_ID" in data.columns:
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data.rename(columns={"ACCOUNT_ID": "ACCOUNT_UID_AUX"}, inplace=True)
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data["ACCOUNT_UID"] = data.apply(
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lambda x: (
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x["ACCOUNT_ID"] if pd.isnull(x["ACCOUNT_UID"]) else x["ACCOUNT_UID"]
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x["ACCOUNT_UID_AUX"]
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if pd.isnull(x["ACCOUNT_UID"])
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else x["ACCOUNT_UID"]
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),
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axis=1,
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)
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# Rename the column RESOURCE_ID to RESOURCE_UID
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if "RESOURCE_ID" in data.columns:
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data.rename(columns={"RESOURCE_ID": "RESOURCE_UID_AUX"}, inplace=True)
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data["RESOURCE_UID"] = data.apply(
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lambda x: (
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x["RESOURCE_ID"] if pd.isnull(x["RESOURCE_UID"]) else x["RESOURCE_UID"]
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x["RESOURCE_UID_AUX"]
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if pd.isnull(x["RESOURCE_UID"])
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else x["RESOURCE_UID"]
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),
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axis=1,
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)
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# Rename the column "SUBSCRIPTION" to "ACCOUNT_UID"
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if "SUBSCRIPTION" in data.columns:
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data.rename(columns={"SUBSCRIPTION": "ACCOUNT_UID_AUX"}, inplace=True)
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data["ACCOUNT_UID"] = data.apply(
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lambda x: (
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x["SUBSCRIPTION"] if pd.isnull(x["ACCOUNT_UID"]) else x["ACCOUNT_UID"]
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x["ACCOUNT_UID_AUX"]
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if pd.isnull(x["ACCOUNT_UID"])
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else x["ACCOUNT_UID"]
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),
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axis=1,
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)
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@@ -252,8 +261,14 @@ else:
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Input("cloud-account-filter", "value"),
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Input("region-filter", "value"),
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Input("report-date-filter", "value"),
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Input("download_link", "n_clicks"),
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)
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def filter_data(cloud_account_values, region_account_values, assessment_value):
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def filter_data(
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cloud_account_values, region_account_values, assessment_value, n_clicks
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):
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# Use n_clicks for vulture
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n_clicks = n_clicks
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# Filter the data
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filtered_data = data.copy()
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# For all the data, we will add to the status column the value 'MUTED (FAIL)' and 'MUTED (PASS)' depending on the value of the column 'STATUS' and 'MUTED'
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if "MUTED" in filtered_data.columns:
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@@ -310,28 +325,21 @@ def filter_data(cloud_account_values, region_account_values, assessment_value):
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for file in csv_files:
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df = pd.read_csv(file, sep=";", on_bad_lines="skip")
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if "CHECK_ID" in df.columns:
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# This handles the case where we are using v3 outputs
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if "TIMESTAMP" not in df.columns:
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# Rename the column 'ASSESSMENT_START_TIME' to 'TIMESTAMP'
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df.rename(columns={"ASSESSMENT_START_TIME": "TIMESTAMP"}, inplace=True)
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df["TIMESTAMP"] = df["TIMESTAMP"].str.replace("T", " ")
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elif "ASSESSMENT_START_TIME" in df.columns:
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df["ASSESSMENT_START_TIME"] = df["ASSESSMENT_START_TIME"].str.replace(
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"T", " "
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)
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# for each row, we are going to take the ASSESMENT_START_TIME if is not null and put it in the TIMESTAMP column
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df["TIMESTAMP"] = df.apply(
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lambda x: (
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x["ASSESSMENT_START_TIME"]
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if pd.isnull(x["TIMESTAMP"])
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else x["TIMESTAMP"]
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),
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axis=1,
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)
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df["TIMESTAMP"] = pd.to_datetime(df["TIMESTAMP"])
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df["TIMESTAMP"] = df["TIMESTAMP"].dt.strftime("%Y-%m-%d")
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if df["TIMESTAMP"][0] == updated_assessment_value:
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list_files.append(file)
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if "TIMESTAMP" in df.columns or df["PROVIDER"].unique() == "aws":
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# This handles the case where we are using v3 outputs
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if "TIMESTAMP" not in df.columns and df["PROVIDER"].unique() == "aws":
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# Rename the column 'ASSESSMENT_START_TIME' to 'TIMESTAMP'
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df["ASSESSMENT_START_TIME"] = df[
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"ASSESSMENT_START_TIME"
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].str.replace("T", " ")
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df.rename(
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columns={"ASSESSMENT_START_TIME": "TIMESTAMP"}, inplace=True
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)
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df["TIMESTAMP"] = df["TIMESTAMP"].str.replace("T", " ")
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df["TIMESTAMP"] = pd.to_datetime(df["TIMESTAMP"])
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df["TIMESTAMP"] = df["TIMESTAMP"].dt.strftime("%Y-%m-%d")
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if df["TIMESTAMP"][0] == updated_assessment_value:
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list_files.append(file)
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# append all the names of the files
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files_names = []
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for file in list_files:
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@@ -418,18 +426,16 @@ def filter_data(cloud_account_values, region_account_values, assessment_value):
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# Filter REGION
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# Check if filtered data contains an aws account
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# TODO - Handle azure locations
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if "LOCATION" in filtered_data.columns:
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filtered_data.rename(columns={"LOCATION": "REGION"}, inplace=True)
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if "REGION" not in filtered_data.columns:
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filtered_data["REGION"] = "-"
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if "LOCATION" in filtered_data.columns:
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filtered_data.rename(columns={"LOCATION": "REGION"}, inplace=True)
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if region_account_values == ["All"]:
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updated_region_account_values = filtered_data["REGION"].unique()
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elif "All" in region_account_values and len(region_account_values) > 1:
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# Remove 'All' from the list
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region_account_values.remove("All")
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updated_region_account_values = region_account_values
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elif len(region_account_values) == 0:
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updated_region_account_values = filtered_data["REGION"].unique()
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region_account_values = ["All"]
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@@ -574,9 +580,11 @@ def filter_data(cloud_account_values, region_account_values, assessment_value):
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"PASS": "#54d283",
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"INFO": "#2684FF",
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"MANUAL": "#636c78",
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"WARNING": "#fca903",
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"MUTED (FAIL)": "#fca903",
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"MUTED (PASS)": "#03fccf",
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"MUTED (MANUAL)": "#b33696",
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"MUTED (WARNING)": "#c7a45d",
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}
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# Define custom colors
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color_mapping = {
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@@ -617,13 +625,8 @@ def filter_data(cloud_account_values, region_account_values, assessment_value):
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style={"height": "300px", "overflow-y": "auto"},
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)
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# Figure for the bar chart
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color_bars = [
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color_mapping["critical"],
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color_mapping["high"],
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color_mapping["medium"],
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color_mapping["low"],
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color_mapping[severity] for severity in df1["SEVERITY"].value_counts().index
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]
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figure_bars = go.Figure(
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@@ -651,11 +654,17 @@ def filter_data(cloud_account_values, region_account_values, assessment_value):
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)
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# TABLE
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severity_dict = {"critical": 3, "high": 2, "medium": 1, "low": 0}
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severity_dict = {
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"critical": 4,
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"high": 3,
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"medium": 2,
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"low": 1,
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"informational": 0,
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}
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fails_findings["SEVERITY"] = fails_findings["SEVERITY"].map(severity_dict)
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fails_findings = fails_findings.sort_values(by=["SEVERITY"], ascending=False)
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fails_findings["SEVERITY"] = fails_findings["SEVERITY"].replace(
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{3: "critical", 2: "high", 1: "medium", 0: "low"}
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{4: "critical", 3: "high", 2: "medium", 1: "low", 0: "informational"}
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)
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table_data = fails_findings.copy()
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